NAOC Open IR  > 太阳物理研究部
Coronal Mass Ejections detection using multiple features based ensemble learning
Yin, Jianqin1,2; Yao, Hai2; Lin, Jiaben3; Yin, Yilong4; Zhang, Ling2; Liu, Xiaoli2; Feng, Zhiquan2; Wang, Xiaofan3
2017-06-28
Source PublicationNEUROCOMPUTING
Volume244Pages:123-130
AbstractCoronal Mass Ejection (CME) is a major solar activity that affects the earth, thus CMEs detection is of great importance for space weather forecast, disaster prevention and reduction. We model the detection of Coronal Mass Ejections (CMEs) as the classification of the brightest block in the current running difference image. Because CMEs usually correspond to the areas with high gray values or complex texture features, multiple features including gray features and texture features are extracted to represent the brightest block. And classifier is designed based on these features. Our method includes four steps: first, because the CMEs spread along the radial direction of the sun, in order to facilitate the analysis, the original coordinate is transformed into the polar coordinate; Secondly, because the typical appearance of the CMEs is bright or complex texture enhancement, we use the brightest block to represent the whole image; Thirdly, we extract the gray, texture and HOG features of the brightest gray blocks. Finally, we use the extracted features to design decision trees as the base classifiers, and AdaBoost is used to obtain the final ensemble classifier. As far as we know, this is the first time that the learning based classification framework is presented in the CMEs detection. Moreover, multiple feature fusion is first used to model the various CMEs. Experimental results show that the integration of multi-feature based detection algorithm proposed can achieve better detection results. (C) 2017 Elsevier B.V. All rights reserved.
SubtypeArticle
KeywordCoronal Mass Ejections Detection Multiple Features Fusion Ensemble Learning
WOS HeadingsScience & Technology ; Technology
Funding OrganizationNational Natural Science Foundation of China(61673192 ; National Natural Science Foundation of China(61673192 ; National Natural Science Foundation of China Joint Fund ; National Natural Science Foundation of China Joint Fund ; Guangdong Key Project(U1201258) ; Guangdong Key Project(U1201258) ; Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; 61472163 ; 61472163 ; 61573219) ; 61573219) ; National Natural Science Foundation of China(61673192 ; National Natural Science Foundation of China(61673192 ; National Natural Science Foundation of China Joint Fund ; National Natural Science Foundation of China Joint Fund ; Guangdong Key Project(U1201258) ; Guangdong Key Project(U1201258) ; Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; 61472163 ; 61472163 ; 61573219) ; 61573219)
DOI10.1016/j.neucom.2017.03.030
WOS KeywordAUTOMATIC DETECTION ; TRACKING ; CMES ; CLASSIFICATION ; CATALOG
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61673192 ; National Natural Science Foundation of China(61673192 ; National Natural Science Foundation of China Joint Fund ; National Natural Science Foundation of China Joint Fund ; Guangdong Key Project(U1201258) ; Guangdong Key Project(U1201258) ; Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; 61472163 ; 61472163 ; 61573219) ; 61573219) ; National Natural Science Foundation of China(61673192 ; National Natural Science Foundation of China(61673192 ; National Natural Science Foundation of China Joint Fund ; National Natural Science Foundation of China Joint Fund ; Guangdong Key Project(U1201258) ; Guangdong Key Project(U1201258) ; Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; 61472163 ; 61472163 ; 61573219) ; 61573219)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000400040100012
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/8857
Collection太阳物理研究部
Affiliation1.Beijing Univ Posts & Telecommun, Sch Automat, Beijing 100876, Peoples R China
2.Univ Jinan, Sch Informat Sci & Engn, Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Peoples R China
3.Chinese Acad Sci, Key Lab Solar Act, Beijing 100012, Peoples R China
4.Shandong Univ, Sch Comp Sci & Technol, Jinan 250002, Peoples R China
Recommended Citation
GB/T 7714
Yin, Jianqin,Yao, Hai,Lin, Jiaben,et al. Coronal Mass Ejections detection using multiple features based ensemble learning[J]. NEUROCOMPUTING,2017,244:123-130.
APA Yin, Jianqin.,Yao, Hai.,Lin, Jiaben.,Yin, Yilong.,Zhang, Ling.,...&Wang, Xiaofan.(2017).Coronal Mass Ejections detection using multiple features based ensemble learning.NEUROCOMPUTING,244,123-130.
MLA Yin, Jianqin,et al."Coronal Mass Ejections detection using multiple features based ensemble learning".NEUROCOMPUTING 244(2017):123-130.
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